The satisfaction of electricity customers and environmental constraints imposed have made the trend towards renewable energies making them more essential due to their advantages as reducing power losses and ameliorating system’s voltage profiles and reliability. This article addresses the optimal location and size of multiple distributed generations (DGs) based on solar photovoltaic panels (PV) connected to electrical distribution network (EDN) using the various proposed hybrid particle swarm optimization (PSO) algorithms based on chaotic maps and adaptive acceleration coefficients. These algorithms are implemented to optimally allocate the DGs based PV (PV-DG) into EDN by minimizing the multi-objective function (MOF), which is represented as the sum of three technical parameters of the total active power loss (TAPL), total voltage deviation (TVD), and total operation time (TOT) of overcurrent relays (OCRs). The effectiveness of the proposed PSO algorithms were validated on both standards IEEE 33-bus, and 69-bus. The optimal integrating of PV-DGs into EDNs reduced the TAPL percentage by 56.94 % for the IEEE 33-bus and by 61.17 % for the IEEE 69-bus test system, enhanced the voltage profiles while minimizing the TVD by 37.35 % and by 32.27 % for two EDNs, respectively.

Goal. The integration of photovoltaic distributed generations in the active distribution network has raised quickly due to their importance in delivering clean energy, hence, participating in solving various problems as climate change and pollution. Adding the battery energy storage systems would be considered as one of the best choices in giving solutions to the mentioned issues due to its characteristics of quick charging and discharging, managing the quality of power, and fulfilling the peak of energy demand. The novelty of the proposed work is the development of new multi-objective functions based on the sum of the three technical parameters of total active power loss, total voltage deviation, and total operation time of the overcurrent protection relay. Purpose. This paper is dedicated for solving the allocation problem of hybrid photovoltaic distributed generation and battery energy storage systems integration in the standard IEEE 33-bus and IEEE 69-bus active distribution networks. Methodology. The optimal integration of the hybrid systems is formulated as minimizing the proposed multi-objective functions by applying a newly developed metaheuristic technique based on various chaotic grey wolf optimization algorithms. The applied optimization algorithms are becoming increasingly popular due to their simplicity, lack of gradient information needed, ability to bypass local optima, and versatility in power system applications. Results. The simulation results of both test systems confirm the robustness and efficiency of the chaotic logistic grey wolf optimization algorithm compared to the rest of the algorithms in terms of convergence to the global optimal solution and in terms of providing the best and minimum multi-objective functions-based power losses, voltage deviation and relay operation time values. Practical significance. Recommendations have been developed for the use of optimal allocation of hybrid systems for practical industrial distribution power systems with the renewable energy sources presence.

In the last few years, the integration of renewable distributed generation (RDG) in the electrical distribution network (EDN) has become a favorable solution that guarantees and keeps a satisfying balance between electrical production and consumption of energy. In this work, various metaheuristic algorithms were implemented to perform the validation of their efficiency in delivering the optimal allocation of both RDGs based on multiple photovoltaic distributed generation (PVDG) and wind turbine distributed generation (WTDG) to the EDN while considering the uncertainties of their electrical energy output as well as the load demand’s variation during all the year’s seasons. The convergence characteristics and the results reveal that the marine predator algorithm was effectively the quickest and best technique to attain the best solutions after a small number of iterations compared to the rest of the utilized algorithms, including particle swarm optimization, the whale optimization algorithm, moth flame optimizer algorithms, and the slime mold algorithm. Meanwhile, as an example, the marine predator algorithm minimized the seasonal active losses down to 56.56% and 56.09% for both applied networks of IEEE 33 and 69-bus, respectively. To reach those results, a multi-objective function (MOF) was developed to simultaneously minimize the technical indices of the total active power loss index (APLI) and reactive power loss index (RPLI), voltage deviation index (VDI), operating time index (OTI), and coordination time interval index (CTII) of overcurrent relay in the test system EDNs, in order to approach the practical case, in which there are too many parameters to be optimized, considering different constraints, during the uncertain time and variable data of load and energy production.

In this article is proposed a new approach based on an Arithmetic optimization Algorithm (AOA) that can notably envisage with diverse problems for optimal installation (locate and size) of Distribution Static Compensator (DSTATCOM) in Electrical Distribution System (EDS). An approach applied for optimal installation of DSTATCOM units in EDS is used as the new multi-objective functions to minimize the Active Power Loss (APL) and various voltage stability indices. The voltage stability indices applied are the Voltage Stability Index (VSI), the Fast Voltage Stability Index (FVSI), the Line Stability Factor (LPQ), and the Power-Voltage Stability Index (PVSI). The methodology is tested on IEEE 33-, and 69-bus EDSs. The results of the simulation confirm that the optimal installation of DSTATCOM plays a significant role in EDS, where excellently reduced the power losses and enhanced the voltage profiles.

A big interest in the last few decades was about the integration of multiple PV sources-based DG units (PV-DG) into Electrical Distribution System (EDS), and this is for their benefits in enhancing power system reliability and operation. To reach the maximum of those benefits, an optimal location and sizing of PV-DG sources into EDS should be properly designed and developed. This paper consists to apply various algorithms of Particle Swarm optimization (PSO) and choosing the best among them to optimally locate and size the PV-DG sources and identify their impact on protection system in EDS, using a Multi Objective Functions (MOF) that aim for minimizing the three technical parameters of Total Active Power Loss (TAPL), Total Voltage Deviation (TVD), and Total Operation Time (TOT) of Non-Standard Over-Current Relays (NS-OCR) based new time-current-voltage tripping characteristic. This work was applied on the two test systems, the IEEE 33-bus, and the IEEE 69-bus. The obtained results show that, the optimal location and sizing of PV-DG sources in the EDS deliver a positive impact in terms of minimizing the active power losses and improving the voltage profile, but a negative one in the part of system protection, exactly the overcurrent relays coordination.

Optimal power flow (OPF) is one of the fundamental mathematical tools currently used to operate power systems within the technical limits of the transmission power system. To determine OPF, a highly non-linear complex problem, it is essential to research power system planning and control. This study presents a practical and trustworthy optimization approach for the OPF problem in electrical transmission power systems. Many intelligence optimization algorithms and methods have recently been developed to solve OPF, particularly the non-linear complex optimization problems. In this paper, a novel meta-heuristic algorithm called the mountain gazelle optimizer (MGO) is suggested for solving the OPF problem. The suggested algorithm applies the improved three single objective functions to the MGO algorithm for the best OPF issue control variable settings. Three objective functions that reflect the minimization of generating fuel cost, the minimizing of active power loss, and the minimizing of voltage deviations have been used to investigate and test the proposed algorithm on the standard IEEE 30-bus test system. The simulation results demonstrate the efficiency of the proposed MGO algorithm; the fuel costs are reduced by 11.407%, power losses are considerably decreased by 51.016%, and the voltage profile is significantly reduced by 91.501%. Furthermore, the outcomes produced by the proposed algorithm have also been contrasted with outcomes produced by applying other comparable optimization algorithms published in recent years. The optimal results are encouraging and demonstrate the resilience and efficacy of the suggested strategy.